AgeratumĪgeratums are also known as the floss flower. They like sunlight, and some species are actually evergreen. The flower for an extended period of time, and they make a good border plant or addition to your container garden. These showy flowers are produced in colors such as blue and purple. AgapanthusĪgapanthus comes in a variety of colors and heights. These flowers are great in cut flower arrangements or in the garden or as a container plant. They are characterized by generally having a darker center, and the leaves are silvery in color. The African Daisy is a perennial with flowers that come in a variety of colors including red, gold, and blue. The scapes can grow tall, often getting to be at least three feet high, making them a great addition to the back of flower beds. The flowers tend to be blue-purple or yellow in color, and the plant is native to mountainous areas. AconiteĪconite is a poisonous plant that is beautiful, which brings many people to plant it in their gardens. They can get to be seven feet high if not properly pruned. These plants are delicate and tender, growing best in sheltered gardens away from the cold frosts of northern climates.Īlso known as wattle, these tend to flower in early spring. AcaciaĪcacias are soft, yellow flowers that tend to be fluffy. The shrub may grow to around 15 feet, and many hybrids have been developed that you can enjoy. The blooms are saucer-shaped, and the stems have gray, hairy leaves. This plant produces flowers that may be white to a purple blue color. Model.layers and set layer.Abutilon is a shrub that blooms during the summer. In this case, you would simply iterate over Here are two common transfer learning blueprint involving Sequential models.įirst, let's say that you have a Sequential model, and you want to freeze all If you aren't familiar with it, make sure to read our guide Transfer learning consists of freezing the bottom layers in a model and only training Transfer learning with a Sequential model ones (( 1, 250, 250, 3 )) features = feature_extractor ( x ) output, ) # Call feature extractor on test input. get_layer ( name = "my_intermediate_layer" ). Sequential ( ) feature_extractor = keras. Quickly creating a model that extracts the outputs of all intermediate layers in a These attributes can be used to do neat things, like Once a Sequential model has been built, it behaves like aĪnd output attribute. See ourįeature extraction with a Sequential model
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